Anomaly detection in multi-attributed networks has become increasingly important and has significant implications in various domains, such as intrusion detection, botnet detection, financial fraud detection and event ...
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Anomaly detection in multi-attributed networks has become increasingly important and has significant implications in various domains, such as intrusion detection, botnet detection, financial fraud detection and event detection. However, detecting rare anomalous nodes is always challenging in a multi -attributed network with a large set of data observations without labels. In this work, we address this problem by learning a graph deep autoencoder framework named as GDAE. The GDAE first jointly takes both the network structure and node attributes as input to calculate the embedding for every node in multi-attributed networks using a deep attention mechanism by attending to its neighbor nodes. Meanwhile, through multiple layers of nonlinear transformations, the GDAE efficiently captures the non-linearity of data and the complex interactions of both node attribute information and network structure information. GDAE then focuses on conducting the decoders of both network structure and node attributes based on the learned node embedding. Subsequently, the reconstruction errors based on the aforementioned encoder and decoder operations are employed to discover the anomalies in multi-attributed networks. Extensive experiments using four real-world datasets from different domains demonstrate that our approach performs superior over representative baseline approaches.(c) 2022 Elsevier B.V. All rights reserved.
In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, ...
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In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, the number of training cells, and the probability of false alarm are set improperly in the conventional CFAR algorithm, the target detection performance is severely degraded. Therefore, we propose a method using a convolutional neural network-based autoencoder (AE) to replace the CFAR algorithm in the multiple-input and multiple-output FMCW radar system. In the AE, the entire detection result is compressed at the encoder side, and only significant signal components are recovered on the decoder side. In this work, by changing the number of hidden layers and the number of filters in each layer, the structure of the AE showing a high signal-to-noise ratio in the target detection result is determined. To evaluate the performance of the proposed method, the AE-based target detection result is compared with the target detection results of conventional CFAR algorithms. As a result of calculating the correlation coefficient with the data marked with the actual target position, the proposed AE-based target detection shows the highest similarity with a correlation of 0.73 or higher.
Evaluation measures of Generative Adversarial Networks (GANs) have been an active area of research and, currently, there are several measures to evaluate them. The most used GANs evaluation measure is the Frechet Ince...
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Evaluation measures of Generative Adversarial Networks (GANs) have been an active area of research and, currently, there are several measures to evaluate them. The most used GANs evaluation measure is the Frechet Inception Distance (FID). Measures such as FID are known as model-agnostic methods, where the generator is used as a black box to sample the generated images. Like other measures of model-agnostic, FID uses a deep supervised model for mapping real and generated samples to a feature space. We proposed an approach here with a deep unsupervised model, the Vector Quantised-Variational autoencoder (VQ-VAE), for estimating the mean and the covariance matrix of the Frechet Distance and named it Frechet autoencoder Distance (FAED). Our experimental results highlighted that the feature space of the VQ-VAE describes a clustering domain-specific representation more intuitive and visually plausible than the Inception network used by the benchmark FID.
Recommender systems are crucial in the big data era, effectively mitigating information overload. Existing recommendation methods are limited on highly sparse data and have mediocre recall performance. Group influence...
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Recommender systems are crucial in the big data era, effectively mitigating information overload. Existing recommendation methods are limited on highly sparse data and have mediocre recall performance. Group influence aggregates knowledge from different users or organizations to generate decisions, improving information fusion efficiency and group decision-making quality. In this paper, a group influence-based deep adversarial autoencoder (GI-AAE), is proposed for top-N recommendation. It leverages group influence to strengthen autoencoder latent features and address sparse data and uses adversarial learning from GANs to enhance reconstruction. The group influence based deep autoencoder (GI-AE) is the generative model for the GI-AAE. Experimental results show that the proposed algorithm is competitive and gets higher recall values.
Intelligent fault diagnosis based on deep learning has been more attractive in practical engineering. However, its accuracy is constrained by unlabeled data and large domain shift for cross-domain fault diagnosis. As ...
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Intelligent fault diagnosis based on deep learning has been more attractive in practical engineering. However, its accuracy is constrained by unlabeled data and large domain shift for cross-domain fault diagnosis. As an unsupervised learning network, the autoencoder (AE) can relieve the pressure of unlabeled data. Using it as a building block, this paper presents a novel deep clustering network, named as clustering graph convolutional network with multiple adversarial learning (c-GCN-MAL) for intelligent fault diagnosis of various bearings. First, multiple representation of datasets, i.e., the datasets and their structured relatedness, are extracted by AE and graph convolutional network (GCN) to recognize different classes and their related information. Then, the dataadversarial and domain-adversarial are individually set by newly defining the loss function of the proposed deep network, so that its clustering and transfer ability for new dataset can be enhanced. In experiments, it is applied to four open-source bearing datasets and one dataset of practical bearings. Transfer tasks between different rotation speeds, damage generation, and experimental bearings, even the transfer from experimental bearings to wheelset bearings used in real tests, are assigned to verify the effectiveness of the proposed network. The experimental results and comparisons indicate that the proposed deep clustering network is feasible for the transfer with large domain shift and can provide noticeably higher accuracy and stable results for cross-domain fault diagnosis of bearings.
As online courses are more and more widely used in college courses, the analysis of online learning data has become an important means to improve teaching quality and learning effect. In this paper, an online learner ...
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ISBN:
(纸本)9798400711732
As online courses are more and more widely used in college courses, the analysis of online learning data has become an important means to improve teaching quality and learning effect. In this paper, an online learner clustering algorithm that integrates autoencoder and Canopy-Kmeans is proposed. The autoencoder is used to perform feature degradation and feature extraction for high-dimensional data which effectively solves the problems of data sparsity and dimensionality disaster. The Canopy algorithm is introduced to precluster the data which reduces the randomness of the K-means algorithm during initialization and improves the stability of clustering. And the improved K-means algorithm is used to perform the clustering analysis of text data. The experimental results show that the algorithm proposed behaved better in both clustering accuracy and noise resistance compared with traditional clustering algorithm, which is helpful to a better understanding of the behavioral characteristics of online learners and a better support for personalized teaching is provided based on this.
In this paper, we propose a method to compress electroencephalography (EEG) data using a differentiable neural network trained with the dynamic time warping (DTW) algorithm. DTW is a popular method for aligning time s...
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In this paper, we propose a method to compress electroencephalography (EEG) data using a differentiable neural network trained with the dynamic time warping (DTW) algorithm. DTW is a popular method for aligning time series data, but it is computationally expensive and not differentiable, which makes it difficult to optimize using gradient-based methods. To circumvent this difficulty, we use a neural network based approximation of DTW as the reconstruction loss to learn a convolutional auto-encoder to compress EEG signals. We show that using a custom DTW as reconstruction loss for the autoencoder improves the sleep staging score of the reconstructed data, suggesting that it allows to better preserve important features of EEG data. We also show that these findings can be extended to multiple datasets and compression ratios.
The increasing need for safe internet access that withstands against various malicious attacks has gained much attention, especially in this abundant information age. Network intrusion detection systems have been prop...
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The increasing need for safe internet access that withstands against various malicious attacks has gained much attention, especially in this abundant information age. Network intrusion detection systems have been proposed to tackle these kinds of attacks. Various machine learning algorithms, including deep learning, have been utilized in network intrusion detection systems. However, the limitations of current feature extraction methods for feature representation learning result in low detection accuracy. Our method utilizes an autoencoder to extract the low-level features in order to provide the necessary information for the classifier. We propose a weight embedding autoencoder to share feature representations between the autoencoder and the classifier. We apply our proposed method to improve two types of networks, a weight embedding autoencoder with a multi layers neural network (WE-AE DNN) and a weight embedding autoencoder with a convolutional neural network (WE-AE CNN) as the classifier. Experiments on two demanding benchmark datasets, such as NSL-KDD and UNSW-NB15 show the effectiveness and superiority of our proposed algorithm in terms of accuracy. The WE-AE DNN and WE-AE CNN improve the accuracy by 0.4% and 0.5% on NSL-KDD, respectively. Meanwhile, on the UNSWNB15 dataset, the WE-AE DNN and WE-AE CNN improve the accuracy by 2.8% and 0.5%, respectively.
Conventional blockchain technologies developed for cryptocurrency applications involve complex consensus algorithms which are not suitable for resource constrained Internet of Things (IoT) devices. Therefore, several ...
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Conventional blockchain technologies developed for cryptocurrency applications involve complex consensus algorithms which are not suitable for resource constrained Internet of Things (IoT) devices. Therefore, several lightweight consensus mechanisms that are suitable for IoT devices have been proposed in recent studies. However, these lightweight consensus mechanisms do not verify the originality of the data generated by the IoT devices, so false and anomalous data may pass through and be stored in the ledger for further analysis. In this work to address the data originality verification problem, we propose an autoencoder (AE)-integrated Chaincode (CC)-based consensus mechanism in which the AE differentiates normal data from anomalous data. The AE is invoked through the CC once a transaction is initiated;the result returned from the AE to the CC is stored in the ledger. We have conducted a case study to train and test the AE model on the IoTID20 dataset. Also, Minifabric (MF) is used to implement the CC and illustrate the CC operation that stores only original IoT data. Moreover, the performance has been shown for the CC in terms of latency and throughput.
Background and objectiveThe classification of glioma subtypes is essential for precision therapy. Due to the heterogeneity of gliomas, the subtype-specific molecular pattern can be captured by integrating and analyzin...
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Background and objectiveThe classification of glioma subtypes is essential for precision therapy. Due to the heterogeneity of gliomas, the subtype-specific molecular pattern can be captured by integrating and analyzing high-throughput omics data from different genomic layers. The development of a deep-learning framework enables the integration of multi-omics data to classify the glioma subtypes to support the clinical *** and methylome data of glioma patients were preprocessed, and differentially expressed features from both datasets were identified. Subsequently, a Cox regression analysis determined genes and CpGs associated with survival. Gene set enrichment analysis was carried out to examine the biological significance of the features. Further, we identified CpG and gene pairs by mapping them in the promoter region of corresponding genes. The methylation and gene expression levels of these CpGs and genes were embedded in a lower-dimensional space with an autoencoder. Next, ANN and CNN were used to classify subtypes using the latent features from embedding space. CNN performs better than ANN for subtyping lower-grade gliomas (LGG) and glioblastoma multiforme (GBM). The subtyping accuracy of CNN was 98.03% (+/- 0.06) and 94.07% (+/- 0.01) in LGG and GBM, respectively. The precision of the models was 97.67% in LGG and 90.40% in GBM. The model sensitivity was 96.96% in LGG and 91.18% in GBM. Additionally, we observed the superior performance of CNN with external datasets. The genes and CpGs pairs used to develop the model showed better performance than the random CpGs-gene pairs, preprocessed data, and single omics *** current study showed that a novel feature selection and data integration strategy led to the development of DeepAutoGlioma, an effective framework for diagnosing glioma subtypes.
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